An innovative neuro-fuzzv modfu for predicting creep ofthf medial collateral ligament
Bibliographic record
Abstract
It is well established that progressive recruitment of the collagen fibres in the knee medial collateral ligament (MCL) leads to the non-linear toe-region of the ligament stress-strain curve. It has also been argued that fibre recruitment helps the ligament to lessen and resist creep. Minimal creep in ligaments allows maintaining joint equilibrium. This is especially important for the knee stability in regular daily activities like walking or running where loading is repetitively applied to the joint over many cycles. Nevertheless, due to dependency of fibre recruitment on many factors affecting its behaviour, the level of recruitment of the collagen fibres is difficult to quantify using classical modeling techniques. We therefore developed a soft-computing algorithm to model creep of the knee MCL in two steps: first, the ill-defined fibre recruitment is quantified using the fuzzy systems. Second, the fibre recruitment is incorporated along with creep stress and creep time to model creep using a hybrid neuro-fuzzy system. The model is trained and tested using experimental database including creep tests and crimp image analysis. The model showed very promising results and confirmed the role of fibre recruitment in viscoelastic behaviour of the ligament.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".